Description

Book Synopsis

From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture.

This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.



Table of Contents
1. Introduction.- 2. Data Pre-Processing and Modeling Factors.- 3. Common Demand Prediction Methods.- 4. Tree-Based Methods.- 5. Clustering Techniques.- 6. Evaluation and Visualization.- 7. More Advanced Methods.- 8. Conclusion and Advanced Topics.

Demand Prediction in Retail: A Practical Guide to

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    A Hardback by Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste

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      Book details

      Publisher Springer Nature Switzerland AG
      Published 22 December 2021
      ISBN-13 9783030858544
      978-3030858544
      ISBN-10 3030858545
      Also in:
      Business, Finance & Law Management of specific areas Purchasing and supply management Sales and marketing

      Description

      Book Synopsis

      From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture.

      This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.



      Table of Contents
      1. Introduction.- 2. Data Pre-Processing and Modeling Factors.- 3. Common Demand Prediction Methods.- 4. Tree-Based Methods.- 5. Clustering Techniques.- 6. Evaluation and Visualization.- 7. More Advanced Methods.- 8. Conclusion and Advanced Topics.

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